Approximate sparse spectral clustering based on local information maintenance for hyperspectral image classification

Sparse spectral clustering (SSC) has become one of the most popular clustering approaches in recent years. However, its high computational complexity prevents its application to large-scale datasets such as hyperspectral images (HSIs). In this paper, we propose two efficient approximate sparse spectral clustering methods for HSIs clustering in which clustering performance is improved by utilizing local information among the data. Firstly, we construct a smaller representative dataset on which sparse spectral clustering is performed. Then the labels of ground object are extending to whole dataset based on the local information according to two extending strategies. The first one is that the local interpolation is utilized to improve the extension of the clustering result. The other one is that the label extension is turned to a problem of subspace embedding, and is fulfilled by locally linear embedding (LLE). Several experiments on HSIs demonstrated that the proposed algorithms are effective for HSIs clustering.

[1]  Bin Gu,et al.  Incremental Support Vector Learning for Ordinal Regression , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Daoqiang Zhang,et al.  Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation , 2007, Pattern Recognit..

[3]  Joaquim Salvi,et al.  Enhanced Local Subspace Affinity for feature-based motion segmentation , 2011, Pattern Recognit..

[4]  Ling Huang,et al.  Fast approximate spectral clustering , 2009, KDD.

[5]  George Loizou,et al.  Computer vision and pattern recognition , 2007, Int. J. Comput. Math..

[6]  René Vidal,et al.  Sparse Subspace Clustering: Algorithm, Theory, and Applications , 2012, IEEE transactions on pattern analysis and machine intelligence.

[7]  Yuhui Zheng,et al.  Image segmentation by generalized hierarchical fuzzy C-means algorithm , 2015, J. Intell. Fuzzy Syst..

[8]  René Vidal,et al.  Segmenting Motions of Different Types by Unsupervised Manifold Clustering , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Ronggao Liu,et al.  Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research. , 2007, Journal of environmental management.

[10]  Minoru Sasaki,et al.  Spectral Clustering for a Large Data Set by Reducing the Similarity Matrix Size , 2008, LREC.

[11]  Guan Yong,et al.  Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm , 2010, 2010 Third International Symposium on Intelligent Information Technology and Security Informatics.

[12]  H. Damasio,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence: Special Issue on Perceptual Organization in Computer Vision , 1998 .

[13]  Gilad Lerman,et al.  Median K-Flats for hybrid linear modeling with many outliers , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[14]  Jian Yang,et al.  Robust nuclear norm regularized regression for face recognition with occlusion , 2015, Pattern Recognit..

[15]  Feng Xiang-chu,et al.  A Survey on Sparse Subspace Clustering , 2015 .

[16]  René Vidal,et al.  Sparse subspace clustering , 2009, CVPR.

[17]  Xinlei Chen,et al.  Large Scale Spectral Clustering Via Landmark-Based Sparse Representation , 2015, IEEE Transactions on Cybernetics.

[18]  Weiwei Sun,et al.  Band Selection Using Improved Sparse Subspace Clustering for Hyperspectral Imagery Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  Allen Y. Yang,et al.  Estimation of Subspace Arrangements with Applications in Modeling and Segmenting Mixed Data , 2008, SIAM Rev..

[20]  Guangliang Chen,et al.  Spectral Curvature Clustering (SCC) , 2009, International Journal of Computer Vision.

[21]  René Vidal,et al.  Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted Trajectories , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Liangpei Zhang,et al.  Spectral–Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Qian Du,et al.  Classification of Hyperspectral Imagery Using a New Fully Convolutional Neural Network , 2018, IEEE Geoscience and Remote Sensing Letters.

[24]  Abdelkader Benyettou,et al.  Gray Wolf Optimizer for hyperspectral band selection , 2016, Appl. Soft Comput..

[25]  Gilad Lerman,et al.  Hybrid Linear Modeling via Local Best-Fit Flats , 2010, International Journal of Computer Vision.

[26]  Farid Melgani,et al.  Clustering of Hyperspectral Images Based on Multiobjective Particle Swarm Optimization , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Yi Yang,et al.  Discriminative Nonnegative Spectral Clustering with Out-of-Sample Extension , 2013, IEEE Transactions on Knowledge and Data Engineering.

[28]  Jitendra Malik,et al.  Spectral grouping using the Nystrom method , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Marc Pollefeys,et al.  A General Framework for Motion Segmentation: Independent, Articulated, Rigid, Non-rigid, Degenerate and Non-degenerate , 2006, ECCV.

[30]  Farhad Samadzadegan,et al.  A Comparison Study Between Two Hyperspectral Clustering Methods: KFCM and PSO-FCM , 2013 .

[31]  Qingyun Dai,et al.  Local information-based fast approximate spectral clustering , 2014, Pattern Recognit. Lett..

[32]  Guangming Shi,et al.  Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation , 2016, IEEE Transactions on Image Processing.